Psychometric Framework for Comparing Alignment- Conditioned Behavior in Large Language Models

This paper introduces an interpretive framework for comparing the behavioral tendencies that post-training alignment induces in large language models (LLMs). Post-training alignment techniques including Supervised Fine-Tuning (SFT), Reinforcement Learning from Human Feedback (RLHF), and constitutional or rule-based prompting strategies steer LLMs toward human-preferred behavior. Using a scenario-based cross-model psychometric framework grounded in the Big Five personality traits (OCEAN: Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism), we infer personality-like profiles for four prominent LLMs. We then map these profiles onto the responsiveness and demandingness dimensions from developmental psychology's parenting-styles theory, positioning each model in a two-dimensional responsiveness–demandingness space. Treating this mapping as an interpretive lens rather than a definitive measure of alignment quality, we examine how models distribute across the four parenting archetypes and discuss the assumptions required to read these positions normatively. This framing offers an exploratory and interpretable notion of "parenting LLMs" for reasoning about alignment regimes, while we explicitly delineate the limits of the analogy. We validate the framework with two independent human annotators on 440 paired ratings; both annotators yield moderate agreement with the LLM-based scoring and the relative ranking of models is preserved across all three rater sources.